Título:	Application of spectral mixture analysis to Amazonian land-use and land-cover classification.
Autor:	LU, D. BATISTELLA, M. MORAN, E. MAUSEL, P.
Afiliación:	DENSHENG LU, INDIANA UNIVERSITY; MATEUS BATISTELLA, CNPM; EMILIO MORAN, INDIANA UNIVERSITY; P. MAUSEL, INDIANA STATE UNIVERSITY.
Año:	2004
Referencia:	International Journal of Remote Sensing, v. 25, n. 23, p. 5345-5358, 2004.
Descripción:	Abundant vegetation species and associated complex forest stand structures in moist tropical regions often create difficulties in accurately classifying land-use and land-cover (LULC) features. This paper examines the value of spectral mixture analysis (SMA) using Landsat Thematic Mapper (TM) data for improving LULC classification accuracy in a moist tropical area in Rondbnia, Brazil. Different routines, such as constrained and unconstrained least-squares solutions, different numbers of endmembers, and minimum noise fraction transformation, were examined while implementing the SMA approach. A maximum likelihood classifier was also used to classify fraction images into seven LULC classes: mature forest, intermediate secondary succession, initial secondary succession, pasture, agricultural land, water, and bare land. The results of this study indicate that reducing correlation between image bands and using four endmembers improve classification accuracy. The overall classification accuracy was 86.6% for the seven LULC classes using the best SMA processing routine, which represents very good results for such a' complex environment. The overall classification accuracy using a maximum likelihood approach was 81.4%. Another finding is that use of constrained or unconstrained solutions for unmixing the atmospherically corrected or raw Landsat TM images does not have significant influence on LULC classification performances when image endmembers are used in a SMA approach.
Palabras clave:	Vegetation species Landsat Thematic Mapper
Tipo de Material:	Artigo de periódico
Acceso:	openAccess
Aparece en las colecciones:	Artigo em periódico indexado (CNPM)